Statewide Visual Geolocalization in the Wild
This addresses the challenge of precise visual geolocalization for applications like mapping and navigation, representing a strong domain-specific advancement.
The paper tackles the problem of geolocating street-view photos within a state-sized region by matching them against aerial imagery, achieving 60.6% localization accuracy within 50 meters for photos in Massachusetts.
This work presents a method that is able to predict the geolocation of a street-view photo taken in the wild within a state-sized search region by matching against a database of aerial reference imagery. We partition the search region into geographical cells and train a model to map cells and corresponding photos into a joint embedding space that is used to perform retrieval at test time. The model utilizes aerial images for each cell at multiple levels-of-detail to provide sufficient information about the surrounding scene. We propose a novel layout of the search region with consistent cell resolutions that allows scaling to large geographical regions. Experiments demonstrate that the method successfully localizes 60.6% of all non-panoramic street-view photos uploaded to the crowd-sourcing platform Mapillary in the state of Massachusetts to within 50m of their ground-truth location. Source code is available at https://github.com/fferflo/statewide-visual-geolocalization.